Knowledge Representation Considerations for a Domain Independent
Semantic Parser
Mark Jones, Patrick Demasco, Kathleen McCoy, & Christopher Pennington
Applied Science and Engineering Laboratories
University of Delaware/ A.I. duPont Institute
Wilmington, Delaware USA
(c) 1991 RESNA Press. Reprinted with permission.
Abstract
We have previously presented the overall system design for the
Compansion system and have discussed further enhancements to the
parsing component that provides domain independent processing. In
this paper, we discuss the knowledge representation scheme currently
under development. The major improvements include a
hierarchical-based lexicon representation, the use of case frame
preferences for word role disambiguation, and improvements to the
parsing logic that increase the overall system robustness.
Background
This work is part of an augmentative communication project being
conducted at the Applied Science and Engineering Laboratories at the
University of Delaware and the A.I. duPont Institute. The goal of this
project is to increase the communication rate of physically disabled
individuals via Natural Language Processing techniques.
We wish to take as input a compressed message (i.e., one containing
mainly the content words of the desired utterance) from the disabled
individual and generate a syntactically and semantically well-formed
sentence. For a description of the Sentence Compansion system and the
Semantic Parser's role in it see, (McCoy et al., 90). This paper
builds on the previous work by describing recent insights into the
knowledge representation scheme used by the semantic parser.
Statement of the Problem
The Semantic Parser (see also (Small &Rieger, 82)) is responsible for
determining the semantic role being played by each input word. It must
determine which word is the verb, what role each noun phrase plays
with respect to the verb (e.g., actor, theme), and what modification
relationships are present.
These inferences must be based on stored knowledge about individual
words and possible word relationships. In our previous efforts, we
utilized a a non-hierarchical word categorization scheme, and
represented possible word relationships with relatively simple
deterministic heuristics. While this approach proved satisfactory for
relatively small vocabularies and simple sentence structures, it
became necessary to consider substantial improvements to this aspect
of the parser. In addition, we wanted to increase the robustness of
the parser to accommodate ill-formed input.
Approach
Our approach is based on a Case Frame-based representation of sentence
structure with word roles stored in hierarchical data structures.
Heuristics employed to fill Case Roles represent uncertainty with
preference scales. These preferences allow us to calculate a total
confidence level for each potential parse. Finally enhancements to the
parser logic allow us to infer the likely role of a word that is not
explicitly stored in the lexicon.
Case Frame Representation
The output of the parser is in the form of Case Frames (Fillmore,
77). The main idea behind case frames is that in a sentence there is a
fixed number of roles that objects can play with respect to the main
verb. Given the input: [John break hammer], the parser will return the
semantic parse below.
((43 DECL
(VERB (LEX BREAK))
(AGEXP (LEX JOHN))
(THEME (LEX HAMMER))
(TENSE PRES)))
This parse is consistent with the sentence, "John breaks the hammer."
The first line gives a confidence value for the parse, and says that
this is a declarative sentence. The second line states that the main
verb of the sentence is break. The third line states that the AGEXP
(doer) of the break action is John. The next line says that what is
being broken is a hammer.
Given the input: [John tell Mary Joke Sue], the parser will return
(among others) the semantic parse below.
(71 DECL
(VERB (LEX TELL))
(AGEXP (LEX JOHN))
(THEME (LEX JOKE))
(GOAL (LEX MARY))
(BENEF (LEX SUE))
(TENSE PRES))
This parse is consistent with the sentence, "John tells a joke to Mary for Sue." There are a few new cases that should be explained. Note that the THEME is joke and not Mary; joke is what is being told. Mary is
the receiver, the GOAL. Finally, the act is done for Sue; she is the BENEFiciary.
Knowledge Representation
The knowledge used by the parser must be as great as possible because
it cannot rely on syntactic information. Also, this knowledge must be
domain independent. The parser utilizes several knowledge hierarchies
of which two are particularly important.
The object hierarchy captures generalizations about nouns. The verb
hierarchy captures generalizations about verbs.The main verb of the
sentence is key in predicting the semantic structure of the
sentence. The layout of the verb hierarchy is motivated by work in
systemic grammar (Halliday85). There are two general types of
heuristics: Those that are semantic in nature, and those that are more
idiosyncratic to the verb, more syntactic in nature.
Idiosyncratic Case Constraints
The idiosyncratic case constraints are called idiosyncratic because
they are attached to individual verbs, rather than inherited. There
are two key properties that can be associated with each verb. They are
Mandatory and Forbidden. For example, the verb hit requires that the
THEME be filled. The mandatory feature allows this to be
represented. On the other hand, hit cannot accommodate a GOAL. This
can also be represented in the system. This relates to traditional
linguistics. Typically intransitive verbs forbid the filling of the
THEME case: die cannot have a theme Words other than bi-transitives
typically forbid filling the goal case: give can have a goal. The most
common situation, that of the verb neither forbidding nor requiring a
particular case, is represented by the absence of either feature.
Semantic Case Preferences
The semantic preferences differ from the syntactic predictions in a
number of ways. First, these semantic preferences are not as definite,
they are much fuzzier in nature, thus the term preference. These
preferences are the basis for the heuristic values given to the output
interpretations. Second, unlike the constraints, these semantic
preferences are general enough to be inherited down a hierarchy of
verbs. Third, these semantic preferences are closely tied to the
object hierarchy. The case constraints have no interest in how the
object knowledge base is structured.
Semantic preferences rely on a numeric scale ranging from 1 for low
preference to 4 for high preference. In this scale, 1 and 4 are for
special cases. 4 signifies that the binding is exceptionally
appropriate. 1 signifies that the binding is only appropriate in
special cases. For normal situations, the ratings of 2 and 3 are
used. At this point, this granularity seems appropriate for our level
of inferencing.
Case Importance Preference - This preference represents how important
it is to fill a particular case in the frame. This is much more
flexible than mandatory and forbidden which were described
previously. For example, with material verbs such as kick, it seems
much more likely that the role of THEME will be filled than that of
BENEFiciary. To represent this, a higher value (3 on the 1 to 4 scale)
is given as the preference of filling the THEME case, while a lower
value (1) is given as the preference for filling the BENEFiciary case.
Case Filler Preference - This preference is directly related to the
object hierarchy. Here, what kinds of objects should be playing the
role is represented, this along with a preference of how reasonable
such a binding seems. For example, the preference for filling the
BENEFiciary case for most verbs is: ((human 3) (organizat 2) (animate
2)). This means that 3 points (again on the 1 to 4 scale) are given
for binding a human in the given role. A binding of organizations,
such as the A.C.L.U., or animate objects yield two points. This
specification may seem ambiguous, is not any human also animate? True,
the solution used in this system is that if a binding can achieve more
than one score, the highest of the scores is used. Also note that a
list such as the one just given is considered exclusive. In this
example, it means that any verb following the stated pattern for
BENEFiciaries will not allow objects that do not have as ancestors one
of the three types given. A chair (inanimate object) would not be
considered as a BENEFiciary.
The inheritance mechanism for the case importance and the case filler
preferences is rather simple. Those preferences stated by the highest
ancestors of the verb hold preferences that are reasonable in
general. If conflicting information is given by more specific (lower)
ancestor of the verb, the more specific information will be
recognized.
Higher-Order Case Preferences - The mechanisms for Fill-Case and the
Fill-Case-with-what preferences are limited in scope only to one role
at a time (e.g., BENEFiciary). The Higher-Order Preferences fill the
need for some more unifying heuristics With this power we can
represent the following: If a non-human animate (e.g., dog) is the
AGENT of a material process, it is quite unlikely that an instrument
is being used.
Unknown Words
The power of this knowledge representation scheme iprovides robustness
in parsing ability. The system is able to make some sense out of
unknown words present in the input stream. If the parser knows the
main verb of the sentence it can infer the role of the unknown word
and the type of object that is represented. The parser assumes that an
unknown word is an object. It then creates multiple senses of the
unknown word; one sense for place, tool, food, etc. These senses are
chosen to cover the range of objects yet not be too specific. Because
multiple word senses are treated as mutually exclusive, it tries each
sense separately. The heuristic ratings allow the interpretation(s)
with the best word sense to rise to the top, and a moderately
intelligent guess of the unknown word is achieved. The information
inferred about the unknown word can be passed on to and referenced by
the processes that follow the semantic parser. The table below lists
several examples followed by the case that the unknown word (XXX) is
interpreted to fill, and the type of object in the object hierarchy
that the object is interpreted to be.
[John break window XXX] INSTR tool
[John eat XXX fork] THEME ingestible
[John eat pizza XXX] INSTR tool
[John tell XXX Mary] THEME abstract
[John go XXX] LOC place
[XXX carry paper] AGEXP animate and
ergative-object (tie)
Implications
This approach takes advantage of several important
generalizations. First, the object and verb hierarchies capture needed
generalizations. Also, the preferences are distributed among the verbs
in a motivated manner. This approach lends elegance to the system,
and makes it easier to enhance.
Distinctions between the knowledge have been well placed. First,
separating the idiosyncratic constraints (what roles must and must not
be filled) from the preferences (what roles should be filled, and with
what) is useful. This is key to the elegance of the knowledge
hierarchies, because such behaviors cut across different
dimensions. The notion of transitivity was used to explain the more
syntactic knowledge. Such knowledge as transitivity clearly cuts along
a different dimension than that of meaning. For example, the parser
encodes the words eat and swallow as semantically equivalent. However,
swallow cannot typically have an instrument, while eat may.
Previous approaches to representing such knowledge have not
distinguished between that captured in case importance and case filler
preferences. Without this distinction, statements such as those of
case importance cannot be made. Recall, that for many material verbs,
such as hit, filling the THEME role is very important. But without
such information, the parser would mistakenly consider Mary in [John
hit Mary] a BENEFiciary, because, other things being equal, people are
highly correlated to the BENEFiciary role. Another major advantage of
this system is its use of heuristics. Not only can the system handle
[John break hammer], but also [John break hammer window], where hammer
now plays a different role (Instrument). Through its robust
heuristics, it can recognize the preferred interpretation of this
message.
Discussion
Although the parser has been radically changed in the last year, it
already captures the functionality of the previous system, including
inferring agents and verbs in some situations.
With these theoretical improvements in the semantic parser, we come
closer to our goal of making available an augmentative communication
system which takes advantage of the power of research in the field of
Natural Language Processing.
Acknowledgments
This work is supported by Grant Number H133E80015 from the National
Institute on Disability and Rehabilitation Research. Additional
support has been provided by the Nemours Foundation.
References
C.J. Fillmore.The case for case reopened. In P. Cole and J.M. Sadock,
editors, Syntax and Semantics VIII Grammatical Relations, pages 59-81,
Academic Press, New York, 1977.
Halliday M. A. K. An Introduction to Functional Grammar. Edward
Arnold, London England, 1985
K. McCoy, P. Demasco, M. Jones, C. Pennington, and C. Rowe. A Domain
Independent Semantic Parser For Compansion. In Proceedings of the 13th
Annual RESNA conference, pages 187-188, RESNA, Washington, DC., June
1990.
S. Small and C. Rieger. Parsing and comprehendingwith word experts (a
theory and its realization). In Wendy G. Lehnert and Martin H. Ringle,
Editors, Strategies for Natural Language Processing, 1982.
Contact
Mark Jones
Applied Science and Engineering Laboratories
A.I. duPont Institute
P.O. Box 269
Wilmington, DE 19899
Email: jones@udel.edu